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July 27, 2026

Hiding in Plain Sight: Uncovering a Multi-Stage Ransomware Attack Through Behavioral Detection

Darktrace detected a multi-stage ransomware intrusion from its earliest stages, identifying reconnaissance, privilege escalation, lateral movement, command-and-control communications, and data exfiltration before encryption occurred. This analysis highlights how behavioral detection exposed attacker activity using legitimate tools and infrastructure, providing multiple opportunities for early intervention and containment.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Alexandra Evzona
Cyber Analyst
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27
Jul 2026

Why ransomware has changed

Ransomware attacks have continued to increase globally, with 698 incidents reported in May 2026, representing a 48% rise compared to 472 incidents in May 2025 [1]. At the same time, the ransomware landscape is evolving. Several major ransomware groups, including LockBit [2], have been disrupted by successful joint law enforcement operations, resulting in a shift away from a small number of dominant actors towards a more fragmented and distributed ecosystem. This is increasingly composed of smaller groups who play a specialized role in the attack, such as initial access brokers, affiliates or developers.

As a result, ransomware tactics, techniques, and procedures (TTPs) are becoming more diverse and less predictable. On top of this, adversaries are leveraging native tools and legitimate penetration testing frameworks to evade detection. Anomaly-based detection is therefore critical to identify pre-ransomware activity, rather than relying on signatures associated with a handful of well-known ransomware groups.

As these attacks often unfold over several days, there is a critical window for defenders to act. In this context, behavioral-based detection plays a vital role in identifying suspicious pre-ransomware activity, and enabling early intervention before encryption or exfiltration occurs.

Inside a modern ransomware intrusion

In early 2026, Darktrace detected activity within a customer’s environment related to a multi-stage ransomware intrusion from the initial compromise. This activity does not appear to be attributable to a specific ransomware group, and no known ransomware payload was observed until the final stage.

The attack aligns with a broader industry trend in which compromised virtual private network (VPN) credentials are used as an entry point, followed by rapid internal reconnaissance and lateral movement using legitimate administrative tools. This growing preference for native tools and legitimate frameworks in cyber-attacks illustrates that it is increasingly unreliable to depend solely on traditional indicators of compromise such as known malware signatures or exploit detection.

The intrusion also involved the use of Sliver, an open-source adversary emulation framework, which is increasingly observed in real-world attacks. Originally designed for penetration testing and red teaming, Sliver has gained traction among threat actors as a stealthier alternative to more heavily signatured frameworks such as Cobalt Strike. As a legitimate framework, its use further complicates detection for security tools that rely on known malicious signatures.

Darktrace’s detection of a ransomware event in a customer’s environment

The initial compromise appears to have occurred via compromised credentials used over the VPN shortly before, or at the onset of the first indicators of suspicious activity. While it remains unclear as to how or when the threat actors gained access to these credentials, the use of initial access brokers (IABs) is a common feature of modern ransomware operations. This suggests that access to the environment may have been established several days or weeks beforehand.

The intrusion unfolded over three days, presenting multiple opportunities for early detection and intervention before ransomware deployment. The attack progressed through a compressed but structured sequence: initial access and reconnaissance were completed within hours, followed by privilege escalation and lateral movement the next day, and culminating in data exfiltration and encryption shortly thereafter. Throughout each stage, distinct behavioral anomalies emerged across the network providing clear indicators of malicious activity well before the ransomware was deployed.

While Darktrace’s Autonomous Response capability was enabled within the customer’s environment, it was not fully configured across the impacted devices, allowing the attack to progress to ransomware deployment. Had Autonomous Response been fully deployed across the affected systems, it could have taken targeted action against the earliest stages of malicious activity, potentially disrupting the intrusion before it escalated.

Figure 1: Timeline of the attack progression.

Day 1: Reconnaissance and privilege escalation

The threat actor gained access via compromised VPN credentials and initiated internal reconnaissance. Darktrace detected anomalous scanning behavior, including unusual port scanning activity and widespread network enumeration.

Specifically, Darktrace detected a high volume of east-west scanning activity across a broad range of ports, with TCP connections targeting ports 21, 80, 445, 4899 and 8080. Associated URIs suggested the use of Nmap, a widely used penetration testing tool. This highlights how attackers often leverage legitimate penetration testing tools for malicious reconnaissance, enabling them to blend into normal network activity and evade traditional signature-based detection methods.

Figure 2: Darktrace's detection of a sharp increase in anomalous internal connections, triggering multiple high-severity model alerts associated with reconnaissance activity.

Several devices were observed using administrative credentials to carry out privileged actions in a manner that was highly anomalous for the environment. This activity was accompanied by behavior consistent with SMB authentication scanning, suggesting efforts to identify and access additional systems. As the activity intensified, an increasing number of devices became involved, signalling lateral movement and further spread across the network.

Darktrace also identified privilege escalation through active directory (AD) replication abuse, specifically via the drsuapi::DRSGetNCChanges function. This technique allows an attacker with sufficient privileges to request directory replication data from a domain controller (DC), enabling them to extract credentials, including password hashes, without directly interacting with user accounts. Commonly associated with ‘DCSync’ attacks, this technique is frequently used to obtain highly privileged credentials and enable further escalation within an environment.

Figure 3: Darktrace’s detection of anomalous AD replication activity indicative of privilege escalation.

This activity was seen alongside the use of the now obsolete SMBv1, repeated NTLM authentication attempts using multiple variations of ‘Administrator’ credentials, reverse DNS scanning, and large-scale network scanning. Darktrace observed widespread use of SMBv1 across the customer’s environment, exposing a significant security weakness. As a legacy protocol with well-documented weaknesses, SMBv1 can be exploited to facilitate lateral movement, allowing the attackers to expand their access following initial compromise.

Day 3: Lateral Movement, Command & Control, and Exfiltration

Two days later, the attacker escalated privileges and expanded their foothold using living-off-the-land (LOTL) techniques such as PSExec, WMI, and RDP. Concurrently, Darktrace identified command-and-control (C2)-style communications consistent with the Sliver framework, alongside rare outbound connections to cloud infrastructure indicating potential data exfiltration. The volume and severity of observed activity increased as attack behavior intensified.

The device was observed conducting extensive lateral movement, leveraging LOTL techniques to evade detection. Activity included WMI execution (e.g. ExecQuery), DCE-RPC activity, SMB sessions and file writes, most of which were successful, as well as the deployment of PSEXESVC.exe via ADMIN$ shares and prolonged RDP sessions. Darktrace identified this behavior as highly anomalous for the environment. Such activity is commonly associated with the transfer of attacker tooling, remote command execution, and the establishment of persistent access across compromised systems.  

Figure 4: Darktrace’s detection of a spike in RPC binding events indicative of potential lateral movement.

On the same day, Darktrace detected C2-style SSL communications originating from multiple internal devices to rare external endpoints. These connections exhibited anomalous characteristics, including invalid SSL certificates and repeated connection patterns resembling beaconing. Analysis of the observed JA3 fingerprint further linked the activity to Sliver, the adversary simulation framework referenced earlier, as the hash has previously been associated with Sliver-related infrastructure [3]. The use of this framework reflects a broader trend of attackers repurposing legitimate offensive security tools for stealthy C2 communications. Connections to 137[.]220[.]59[.]55 (ASN AS20473 AS-VULTR) indicated that the communications were likely routed via a virtual private server (VPS) hosted by Vultr. Attackers often utilize VPS infrastructure from legitimate cloud providers like Vultr to obscure their true origin, blend into benign traffic, and evade IP-based detection mechanisms [4].

Figure 5: Darktrace’s Cyber AI Analyst detection of two linked unusual connections to Vultr infrastructure.

Darktrace also observed a device initiating SSL connections to safedata.s3[.]wasabisys[.]com, an endpoint associated with Wasabi cloud storage. Darktrace recognized that neither the destination nor the associated IP address had previously been observed within the environment.  More than 200 MB of data was subsequently uploaded externally to endpoints sharing the same JA3 client hash, indicating a sustained transfer session and potential data exfiltration to third-party storage. The apparent exfiltration prior to encryption is consistent with a double-extortion ransomware strategy.

Figure 6: Darktrace’s Cyber AI Analyst detection of more than 30 rare outbound connections to a Wasabi cloud storage endpoint, indicative of potential data exfiltration

Day 4: Encryption

The attack culminated in ransomware deployment, marking the transition from suspicious network activity to a business-impacting incident. Using SMB-based propagation, the threat actor encrypted thousands of files across the network, affecting multiple systems and disrupting normal operations. Throughout the encryption event, the legacy SMBv1 protocol was used extensively across multiple internal systems, resulting in a significant increase in newly encrypted files.

Figure 7: Darktrace’s detection of abnormal spikes in SMB activity and associated model alerts, indicative of ransomware encryption and propagation.

Darktrace’s Cyber AI Analyst automatically investigated and correlated the encryption activity and related events into a single incident narrative, providing the customer with real-time visibility into the attack while significantly reducing investigation time.

Figure 8: Darktrace’s Cyber AI Analyst’s investigation into the encryption activity. AI Analyst incident detailing example encryption activity in real time. Related events are automatically correlated and summarized into a clear narrative, reducing investigation time.

Defender action recommendations

What Could Have Stopped the Attack Earlier?

Although the attack ultimately resulted in ransomware deployment, there were multiple opportunities to detect, contain, and disrupt the intrusion before encryption occurred. The following actions could have significantly reduced the overall impact:

Detect and investigate indicators of reconnaissance and lateral movement

  • Unusual scanning
  • Active Directory replication anomalies consistent with DCSync activity
  • Anomalous use of native tools and processes indicative of LOTL attacks
  • Unusual use of common reconnaissance tools such as Nmap and NetScan

Contain compromised credentials and affected devices

  • Disable and reset compromised VPN credentials
  • Isolate devices performing anomalous scanning and lateral movement activity

Block suspicious external communications and data exfiltration

  • Use anomaly-based detection to detect and block repeated outbound connections to rare external infrastructure
  • Prevent data exfiltration to unauthorized cloud storage services such as Wasabi

Conclusion

The incident highlights the importance of anomaly-based detection, particularly against attacks that primarily use native or legitimate tools to evade traditional security measures. Darktrace identified suspicious activity from the first day of the compromise, providing multiple opportunities to disrupt the intrusion before it progressed to lateral movement and data exfiltration.

In this instance, detection was not the limiting factor; response time was. Prompt investigation and containment of devices exhibiting anomalous behavior could have prevented lateral movement, data exfiltration, and ultimately ransomware deployment.

As adversaries increasingly prioritize stealth over custom malware, relying instead on legitimate tools, valid credentials, and trusted infrastructure, traditional signature-based detection becomes less effective. Identifying subtle behavioral deviations early remains critical to disrupting attacks before they escalate into full-scale ransomware incidents.

Credit to Alexandra Evzona (Cyber Analyst), Priya Thapa (Senior Cyber Analyst)
Edited by Ryan Traill (Content Manager)

Appendices

References

[1] https://industrialcyber.co/ransomware/check-point-reports-ransomware-attacks-jump-48-year-over-year-despite-decline-in-overall-cyberattack-activity/

[2] https://www.europol.europa.eu/media-press/newsroom/news/law-enforcement-disrupt-worlds-biggest-ransomware-operation

[3] https://fieldeffect.com/blog/field-effect-mitigates-not-so-simplehelp-exploits-enabling-deployment-of-backdoors

[4] https://www.darktrace.com/blog/from-vps-to-phishing-how-darktrace-uncovered-saas-hijacks-through-virtual-infrastructure-abuse

Indicators of Compromise (IoCs)

IP Addresses

  • 137[.]220[.]59[.]55 – Potential C2 infrastructure (Vultr AS20473)
  • 38[.]27[.]106[.]123 – Wasabi cloud storage endpoint associated with potential data exfiltration
  • 38[.]27[.]106[.]128 – Wasabi cloud storage endpoint associated with potential data exfiltration
  • 38[.]27[.]106[.]117 – Wasabi cloud storage endpoint associated with potential data exfiltration

Domains

  • safedata[.]s3[.]wasabisys[.]com – Potential data exfiltration endpoint
  • *.wasabisys[.]com – Associated Wasabi cloud storage infrastructure

JA3 Fingerprint

  • d6828e30ab66774a91a96ae93be4ae4c – Associated with the Sliver adversary emulation framework

Files

  • Delete[.]me – File observed during reconnaissance activity, commonly associated with NetScan

Darktrace Model Coverage

·       Anomalous Connection / Active Remote Desktop Tunnel

·       Anomalous Connection / Anomalous Remote Registry Service Control

·       Anomalous Connection / Multiple Failed Windows UDP

·       Anomalous Connection / New or Uncommon Service Control

·       Anomalous Connection / New or Uncommon Service Enumeration

·       Anomalous Connection / New User Agent to IP Without Hostname

·       Anomalous Connection / SMB Enumeration

·       Anomalous Connection / Suspicious Activity On High Risk Device

·       Anomalous Connection / Suspicious Read Write Ratio

·       Anomalous Connection / Sustained MIME Type Conversion

·       Anomalous Connection / Uncommon 1 GiB Outbound

·       Anomalous Connection / Unusual Admin RDP Session

·       Anomalous Connection / Unusual Admin SMB Session

·       Anomalous Connection / Unusual SMB Version 1 Connectivity

·       Anomalous File / EXE from Rare External Location

·       Anomalous File / Internal / Additional Extension Appended to SMB File

·       Anomalous Server Activity / Outgoing from Server

·       Compromise / Beaconing Activity To External Rare

·       Compromise / Ransomware / Possible Ransom Note Read

·       Compromise / Ransomware / Ransom or Offensive Words Written to SMB

·       Compromise / Ransomware / Suspicious SMB Activity

·       Device / Anonymous NTLM Logins

·       Device / Attack and Recon Tools

·       Device / Initial Attack Chain Activity

·       Device / Large Number of Model Alerts

·       Device / Long Agent Connection to New Endpoint

·       Device / Multiple Lateral Movement Model Alerts

·       Device / Network Scan

·       Device / New or Uncommon SMB Named Pipe

·       Device / New or Unusual Remote Command Execution

·       Device / New User Agent

·       Device / Possible RPC Lateral Movement

·       Device / Possible SMB/NTLM Brute Force

·       Device / RDP Scan

·       Device / SMB Lateral Movement

·       Device / SMB Session Brute Force (Non-Admin)

·       Device / SMB Version 1 Access Failures

·       Device / Suspicious File Delete Activity

·       Device / Suspicious Network Scan Activity

·       Device / Unusual SMB Error Detected

·       Device / Unusual SMB To Critical Resource

·       Device / Unusual Winreg Operation

·       Unusual Activity / Multiple Failed Internal Connections

·       Unusual Activity / Possible RPC Recon Activity

·       Unusual Activity / Sustained Anomalous SMB Activity

·       Unusual Activity / Unusual External Data to New Endpoint

·       Unusual Activity / Unusual External Data Transfer

·       Unusual Activity / Unusual File Storage Data Transfer

·       Unusual Activity / Unusual Large Internal Transfer

·       User / New Admin Credentials on Client

·       User / NTLM Login from Unauthenticated Device

Autonomous Response Model Alerts

·       Antigena / Network / External Threat / Antigena Ransomware Block

·       Antigena / Network / External Threat / Antigena Suspicious File Block

·       Antigena / Network / Insider Threat / Antigena Active Threat SMB Write Block

·       Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity

·       Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block

·       Antigena / Network / Insider Threat / Antigena Network Scan Block

·       Antigena / Network / Insider Threat / Antigena Unusual Privileged User Activities Block

·       Antigena / Network / Manual / Quarantine Device

·       Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

·       Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

·       Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

·       Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block 100

·       Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

MITRE ATT&CK MAPPING

Command and control - Protocol Tunneling - T1572

Command and control – Web Protocols – T1071.001

Credential Access- Exploitation for Credential Access- T1212

Credential Access- Password Guessing- T1110

Discovery- File and Directory Discovery- T1083

Discovery- Network Service Discovery- T1046

Discovery- Network Share Discovery- T1135

Discovery- Remote System Discovery- T1018

Exfiltration- Exfiltration Over C2 Channel- T1041

Exfiltration- Exfiltration to Cloud Storage- T1567

Impact- Data Encrypted for Impact- T1486

Impact- Data Encrypted for Impact- T1486

Impact- Service Stop - T1489

Initial Access- Public-Facing Application- T1190

Lateral Movement- Exploitation of Remote Services- T1210

Lateral Movement- Remote Desktop Protocol- T1021

Lateral Movement- SMB/Windows Admin Shares- T1021

Lateral Movement- Taint Shared Content- T1080

Persistence- Modify Registry- T1112

Privilege Escalation- Exploitation for Privilege Escalation- T0890

Privilege Escalation- Valid Accounts- T1078

Reconnaissance- Scanning IP Blocks- T1595

Reconnaissance- Vulnerability Scanning- T1595

Resource Development- Malware- T1588

Stealth- File Deletion- T1070

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Alexandra Evzona
Cyber Analyst

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October 26, 2026

PHP脆弱性、ARTEX、そしてサイバー攻撃におけるAIの役割拡大

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BCVE-2026-109618およびCVE-2026-109619の公開により、PHPを運用している組織は緊急対応を余儀なくされました。どちらの脆弱性も許可のないリモートコード実行を可能にするものであり、対象はPHPが組み込まれ、サードパーティベンダーにアップデートを依存する製品およびアプライアンスも含まれていました[1]。

初期のガイダンスではインターネットに接続されたシステムにパッチを適用し、予期しないコマンド実行やWebroot変更、PHPサーバーからのアウトバウンド接続など、エクスプロイトの成功に関連した振る舞いを監視することに重点が置かれました。

Darktraceは監視中の環境においてスキャニングが広範に発生していることを観測し、これにはPHP脆弱性を標的としたアクティビティに関連すると報告されている数百の個別のIPアドレスが関係していました。スキャニングの量はエクスプロイトの成功を示す証拠の数を著しく上回るものでした。アナリストにとってはこの違いが重要です。スキャニングは露出したインフラへの関心を表すものです。侵害を確認するには、標的とされたシステム上でこれに続く振る舞い、たとえばコマンド実行、ファイル変更、予期しないアウトバウンド接続などの証拠が必要です。

PHPは例外的なケースではありません。その後、Imperva社が脆弱なNode.jsビルドに対するリモートコード実行を悪意あるBrotliデータを通じて実証し、圧縮HTTPレスポンスおよび設定済みリクエストパーサーを通じたコード実行を再現しました。同じ入力はパッチ適用済みのランタイムからは拒否されました。Imperva社はNode2ShellおよびPHP2Shellに対する研究結果を、脆弱性探索とエクスプロイト開発スピードの上昇と結びつけています。[2]

DarktraceはCVE-2026-109618あるいはCVE-2026-109619エクスプロイトの直接的証拠を観測していませんが、10月21日にゼロデイエクスプロイトのアクティビティと一致する過去6か月間で最大の脅威検知を観測し、これは過去のデータと比較して64%上昇を示すものでした。この上昇とPHP脆弱性との関連性は確認されていません。Darktraceの振る舞いベースのアプローチは、特定のエクスプロイトに対して記述されたルールだけに頼ることなく異常なアクティビティを検知するよう設計されています。同様の検知の増加はLog4j脆弱性の公表、およびその他の世界的に重大な脆弱性公表時にも観測されています。

AI支援による脆弱性探索

AIシステムが高い脆弱性探索能力を持つようになるなかで、PHPの脆弱性が発見されました[3]。OpenAIは2026年6月にDaybreakプログラムを発表し、GPT-5.5-CyberおよびCodex Securityをセキュリティ研究者やソフトウェア維持管理者とのパートナーシップと結びつけました。OpenAIはこれらのモデルがブラウザ、ネットワークインフラ、FreeBSDおよびLinuxカーネルの脆弱性の発見およびパッチ作成に適用されていると説明しています。ダークトレースは6月にOpenAI Daybreak Cyber Partner Programに参加し、OpenAIの提供するサイバー能力をDarktrace製品およびサービスにどのように統合できるかを研究しています [4][10]。

OpenAIは8月に、Codex Security Cloudが30,000以上のコードベースにおいて3000万件以上のコミットを分析したと報告しています。OpenAIによると、Cloudflare社はDaybreakを通じてアクセスするモデルを利用して、顧客の承認を受けたコードを調査し、その調査結果をリアルタイムのトラフィックやセキュリティシグナルと組み合わせて、影響を受けたルートがアクティブかどうか、または不審なトラフィックを受信しているかを評価し、顧客によるレビュー対象として緩和策やパッチを提案しているということです[5][6]。

迅速に発見することができても、影響を受けたソフトウェアを特定し、修正をテストし、是正措置を完了するという作業は、依然として防御者が行わなければなりません。PHPの事例は、特に脆弱なランタイムが他の製品に組み込まれている場合に、このギャップが問題となることを明確に示しています。

アタックサーフェスとしての企業AIエージェント

DarktraceSignal Labsは、攻撃者がすでに環境へのアクセス権を取得した後、信頼されたAIエージェントがどのように悪用される可能性があるかについても調査しました。ダークトレースの研究者は管理されたシミュレーション環境において、侵害後のシナリオとして、Claudeプラットフォームのメモリストア機能が悪用され、外部オペレーターと内部のClaude Codeエージェント間にライブC2チャネルを確立できることを実証しました。外部オペレーターは、このC2チャネルを介して、ネットワーク探索、認証情報へのアクセス、データの持ち出しといったタスクをエージェントに動的に割り当てることができました。エージェントのアクティビティの検知は、エージェントハーネスプロセスからのプロセス作成とネットワークトラフィックの相関付け、および予期された動作からの逸脱を特定するためのビヘイビア分析に基づいて行われました。ダークトレースはこのメモリストアを使ったC2テクニックを実際の攻撃で観測したとは主張していません[7]。

日本で観測されたAI支援による攻撃

日本で観測された証拠は、研究用環境を超えて、実際の脆弱性探索と攻撃にAIが使用されていることを示しています。10月21日、JPCERT/CCは最近の日本国内の組織に対する不正アクセスインシデントについての調査結果を発表しました。JPCERT/CCは攻撃者がAIを使って既知の脆弱性をスキャンしAPIを通じて攻撃を実行していることを確認したと報告しています。JPCERT/CCによると、攻撃インフラを調査した結果、Androidアプリケーションの分析、APIの探索、サーバーサイドリクエストフォージェリを含む脆弱性テストの痕跡が確認され、また、この攻撃インフラは日本国外のユーザー向けサービスも標的としていました[8]。

JPCERT/CCはこの公開資料の中ではARTEXを特定していません。ARTEXに対するアトリビューションは、Microsoft社によるStorm-0940の分析を含む、別の調査に基づいています。

攻撃オペレーションにおけるAI

Microsoft社は、ARTEXを限定的な人間の介入でアクティビティを自動化できる、オープンソースのマルチエージェント型ペネトレーションテストシステムと説明しています。Microsoft社は、2021年から活動している中国政府と連携した攻撃者と評価しているStorm-0940が、数百の組織に対してARTEXを使用したと報告し、ConnectWiseのサプライチェーン侵害がStorm-0940によるものであるとしました[9]。

Microsoft社は、ARTEXがスキャン、偵察、パスワードスプレー、OAuthアプリケーションの悪用を加速させていることを確認しました。ARTEXに駆動されたいくつかの攻撃オペレーションは、最初の標的設定から3時間未満で特権クラウドアクセスに到達しました。ConnectWiseの侵害後、Storm-0940は最大で毎分2,900回の頻度で、2,250を超えるテナントへの認証を試みました[9]。

ダークトレースの脅威調査チームは、後にこのキャンペーンとの関連が判明したインフラに関するアクティビティを、Darktraceテレメトリー内で発見しました。10月13日、Darktraceは、Microsoftが後にConnectWiseの顧客環境でサインインおよびMicrosoft Graphのアクティビティを実行していたと特定したアドレスの1つである144.172.64[.]128に関連する認証アクティビティの失敗を記録しました。Darktraceの分析では、約20分間に65回の認証失敗が確認されました。

図1:2026年10月13日、144.172.64[.]128が関与した認証失敗イベントを示すDarktrace Advanced Searchの結果(顧客を特定できる情報は削除済み)。

Darktraceのテレメトリーは、後にこのキャンペーンとの関連が確認されたインフラから、認証の失敗が繰り返し発生していたことを明らかにしています。これはARTEXの実行や侵害の成功を立証するものではありません。観測時点では、Microsoft社による後のアトリビューションについての情報はありませんでした。つまり、アナリストが把握していたのは振る舞いそのものでした。短時間に同一の送信元から認証に繰り返し失敗したということ、および関連するアイデンティティ、アプリケーション、ネットワークティビティについてのデータです。

Microsoft社は、10月10日から14日にかけて、8組織の327アカウントを対象とした、より広範なパスワードスプレー攻撃を報告しました。151,000件を超えるログオン失敗が記録され、なかでも11月10日には111,000件を超えていましたが、このキャンペーンによる認証成功、トークン発行、Graph呼び出し、ディレクトリ変更はいずれもありませんでした。また、Microsoftは、Storm-0940による標的化の大半が成功しなかったとも報告しています[9]。

攻撃者が認証を突破したケースでは、Microsoft社はサービスプリンシパルによるサインインに続いて、Graphおよびディレクトリ操作が行われたことを確認しました。アクティビティには、偵察、アプリケーション認証情報の変更、権限の変更を試みる行為が含まれていました。Microsoft社はサービスプリンシパルによるサインインとGraphアクティビティ、ディレクトリ変更およびリソースアクセスを相関づけることを推奨しています[9]。

アトリビューションの前に振る舞い

PHPとARTEXの事例は、異なる情報から始まっています。PHPの防御担当者は、どのソフトウェアに緊急の対策が必要かを把握していたものの、当初はエクスプロイトついての情報が限られていました。それでも、プロセスの実行、ファイルの変更、外部への通信は、侵害の証拠となったでしょう。

PHPとARTEXの観測結果は、量と影響範囲の違いも示しています。DarktraceはPHPの脆弱性公開時期に広範なスキャンを観測しました。一方ARTEX関連のインフラでは認証アクティビティが集中して発生していました。どちらの観察結果も、それ単独では侵害を実証するものではなく、いずれのケースにおいても、最初のやり取りに続く振る舞いを調べることが調査上の手がかりとなります。

DarktraceのARTEX関連の検知は、認証関連の振る舞いから始まりました。Microsoft社が攻撃インフラのアトリビューションを公表したのはその8日後でした。アトリビューションはコンテキストを提供するものではありましたが、環境内で最初に記録された事象を変更するものではありませんでした。

AIは今や攻撃サイクルの複数のポイントに存在しています。Daybreakは脆弱性の発見と修正にモデルを適用しています。Darktrace Signal Labsは信頼される企業内AIエージェントがどのように悪用されうるかを、管理された環境内で実証しました。JPCERT/CCは最近のインシデントにおけるAI支援による脆弱性探索およびAPI攻撃を確認しました。Microsoft社は実際の攻撃でStorm-0940がARTEXを使用していることを報告しました [4][7][8][9]。

Microsoft社の研究結果はまた、攻撃の規模と影響は別に評価しなければならないことも示しています。ARTEXを使った1つのキャンペーンは151,000回以上のログオン失敗を生成し、アクセスに至らなかったものの、他のキャンペーンでは3時間未満で特権クラウドアクセスに到達した例もありました。セキュリティチームは大量の試行アクティビティと、認証を突破し権限の変更やリソースアクセスへと進むものを区別しなければなりません。

これらのケースを評価するのに必要な証拠は、これまでに良く知られているものです。つまり認証記録、プロセス実行、アプリケーションやディレクトリの変更、ファイルアクティビティやネットワーク通信です。AIはこれらの振る舞いの発生頻度を増大させます。防御者はこのような状況下でもそれらのシーケンスと影響を判断しなければならないのです。

担当:Emma Foulger、Sam Lister、Dylan Evans、Moishe Tajtelbaum

参考資料

[1] NCSC-NL, php2shell vulnerabilities (CVE-2026-109618, CVE-2026-109619), 21 October 2026. https://github.com/NCSC-NL/php2shell

[2] Imperva, https://www.imperva.com/blog/node2shell-from-brotli-data-to-remote-code-execution/

[3] Cyber Security Agency of Singapore, https://www.csa.gov.sg/alerts-and-advisories/alerts/al-2026-151/

[4] OpenAI, Daybreak: Tools for securing every organization in the world, 22 June 2026. https://openai.com/index/daybreak-securing-the-world/

[5] OpenAI Developers, Scaling cyber defenders with Daybreak, 21 August 2026. https://developers.openai.com/blog/scaling-cyber-defenders-with-daybreak

[6] OpenAI, Cloudflare links vulnerability discovery to defense with OpenAI, 21 October 2026. https://openai.com/index/cloudflare-cyber/

[7] Darktrace Signal Labs, Shared Memories as a Covert C2 Channel, draft research supplied for review.

[8] JPCERT/CC, Recent AI-Assisted Cyber Attacks in Japan: Investigating Attack Infrastructure and Techniques, 21 October 2026. https://blogs.jpcert.or.jp/en/2026/10/web_api_attacks.html

[9] Microsoft Security Research and Microsoft Threat Intelligence, AI-driven, large-scale nation-state campaign using ARTEX enables supply chain compromise, 21 October 2026. https://www.microsoft.com/en-us/security/blog/2026/10/21/storm-0940-uses-artex-agentic-ai-connectwise-supply-chain-compromise/

[10] Darktrace, Advancing the Use of Frontier AI in Cybersecurity: Darktrace Joins the OpenAI Daybreak Cyber Partner Program to Explore Defensive AI Integrations, 22 June 2026. https://www.darktrace.com/blog/advancing-the-use-of-frontier-ai-in-cybersecurity-darktrace-joins-the-openai-daybreak-cyber-partner-program-to-explore-defensive-ai-integrations

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Nathaniel Jones
SVP, Global Threat Intelligence

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October 23, 2026

Beyond the Fridge: Exposed Industrial Control Systems

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Introduction

In October 2026, hundreds of supermarkets across France were reportedly affected by a cyberattack targeting their refrigeration systems. According to public reports, the attackers gained access to internet-connected controllers and modified their settings, forcing refrigeration units into high and extremely low temperatures and causing significant disruption. Total losses have been reported to reach several million euros.

The incident drew attention to the security of industrial refrigeration systems, particularly older devices with exposed management interfaces and known vulnerabilities.

The exact details of how the attackers gained access have not been confirmed, however it is speculated that the public-facing web server could be accessed with the username “admin” and password being the current date. Researchers from Claroty’s Team82 identified a vulnerability in Danfoss AK-SM 8xxA Series in 2025, “CVE-2025-41450", allowing for administrative access using a time-based “code-of-the-day" for authentication [1].

Default credentials, outdated firmware and publicly accessible administrative interfaces have been an issue for years. And yet these systems continue to appear in internet scans and require little sophistication to gain admin access. A simple search of internet-connected devices shows a range of exposed devices from Programmable Logic Controllers (PLCs) and remote gateways, to, yes, more fridges.

Darktrace malware researchers identified hundreds of IoT and OT devices publicly exposed on the internet, many of which are configured with no authentication, weak credentials, or default credentials. As shown in Figure 1, an exposed Copeland XWEB web interface, used to manage Dixell equipment, including refrigerators, can provide the ability to remotely control connected devices.

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Figure 1: Copeland XWEB interface used to control systems including refrigerators and air conditioning.

However, it’s not only fridges; web management interfaces for industrial controls are also publicly accessible, often protected only by weak credentials. Figure 2 shows an example of a publicly exposed WAGO interface. The WAGO management interface is a configuration and administration panel for controllers. Through it, a logged-in user can view device details and diagnostics, manage user accounts and passwords, update firmware, back up or restore the full configuration, set the clock, and manage SD card storage. While it typically doesn’t manage switch outputs or read live process values, someone with access could still disrupt operation by changing network or Modbus settings, loading bad firmware or configurations, or by resetting passwords and enabling insecure services.

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Figure 2: WAGO Management interface.

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The issue extends beyond management interfaces. During our research, we identified publicly accessible web interfaces belonging to PLCs, including Rockwell Automation Allen-Bradley devices. In each case, the risk arises from how the devices have been deployed and configured, not from a flaw in the vendors’ products.

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Figure 3: Web interface for Allen-Bradley CompactLogix controller.

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Figure 3 shows the web interface of an Allen-Bradley CompactLogix controller. Unlike the previous examples, this interface provides a view into the controller's operation, including running tasks, network connections, memory usage and diagnostic information. Rockwell Automation's CompactLogix controllers are commonly used to automate industrial processes, from manufacturing and packaging lines to water treatment and material handling systems. Depending on their configuration, these controllers can operate motors, pumps, valves and other physical equipment.

Although in this example the interface shown does not provide direct control over the PLC's logic, it exposes information about the model, firmware, network layout, and connections, which is useful reconnaissance for a bigger attack.

The concern is not necessarily that every exposed interface allows an attacker to immediately manipulate industrial processes. However, these devices are accessible from the public internet in the first place, providing information about systems that may be responsible for controlling physical equipment.

The attacks on refrigeration systems in France highlight the consequences of poorly secured industrial devices. As demonstrated throughout this research, publicly accessible management interfaces are not limited to refrigeration controllers but extend to PLCs and other systems used to manage industrial equipment.

Finding these devices requires little effort. Internet scanners make it possible to identify publicly exposed interfaces quickly, and in some cases these interfaces rely on publicly documented default credentials or have no authentication at all. As attackers increasingly use AI tools, such attacks may be identified and carried out faster and at a greater scale.

The consequences of this access depend on the device and the functionality exposed. While some interfaces provide little more than diagnostic information, others allow users to modify configurations, disrupt communications, or change settings that affect physical equipment. In industrial environments, this can result in operational downtime, damaged equipment and financial losses.

Weak passwords, default credentials and exposed administrative interfaces are not new security issues. Yet their continued presence on internet-connected industrial systems demonstrates how easily preventable weaknesses can leave physical operations vulnerable to attack.

Exposed devices often come from third-party suppliers and contractors, who frequently install refrigeration controllers, gateways and PLCs without the organization ever being aware they are connected to the internet. Asset visibility remains an important foundation, but organizations should assume that some unknown assets and services will exist. Effective security therefore also depends on continuous monitoring, anomaly detection, network segmentation and rapid response, so that risky exposure and unusual activity can be identified and addressed before it becomes disruptive.

Darktrace helps security teams gain visibility across IT and OT and respond to risks: https://www.darktrace.com/products/operational-technology.

Copeland, Dixell, WAGO, Rockwell Automation, Allen-Bradley and CompactLogix are trademarks of their respective owners. Their use here is for identification only and does not imply any affiliation with or endorsement by those owners.

References

1.        https://claroty.com/team82/research/freeze-the-controller-defrost-the-food-uncovering-vulnerabilities-in-danfoss-refrigeration-controllers

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About the author
Tara Gould
Malware Research Lead
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